Fault prediction in hydraulic systems

ABSTRACT

Fault prediction improvements are provided herein via pruning a dataset by selecting a subset of the dataset for use as a pruned dataset, wherein the pruned dataset includes pressure data for a hydraulic system correlated in time with command signals for a plurality of deployment events of the hydraulic system, and in response to determining that a given pressure value for a given deployment event in the pruned dataset satisfies a low pressure threshold and that a change in pressure values over time during the given deployment event satisfies a pressure slope threshold, generating a service message for the hydraulic system that indicates that the hydraulic system is in a prefault state.

FIELD

The present disclosure relates to fault prediction, and morespecifically to improvements in computing devices used to determine whenhydraulic systems are in a prefault state.

BACKGROUND

Hydraulic systems use a contained fluid to apply forces over distancesand/or with mechanical advantage based on the applied pressures andsurface areas within a hydraulic actuator. Hydraulic systems are used,for example, in brake systems to transfer a force applied to a brakepedal to brake pads in various vehicles, in hydraulic actuators tocontrol arms of construction equipment, extend landing gear in aircraft,remotely open doors, etc. The fluid contained in the hydraulic system(i.e., the hydraulic fluid) may be one of various fluids with acoefficient of compressibility of zero or nearly zero, such as water ora mineral oil.

Some solutions for fault detection in hydraulic systems includepreventative maintenance schedules and alerts based on raw pressure orvolume data. However, preventative maintenance schedules merely providegeneral guidelines for maintaining hydraulic systems that may result inunneeded maintenance being performed.

SUMMARY

The present disclosure provides in one embodiment, a method for faultprediction in hydraulic systems, comprising: pruning a dataset byselecting a subset of the dataset for use as a pruned dataset, whereinthe pruned dataset includes pressure data for a hydraulic systemcorrelated in time with command signals for a plurality of deploymentevents of the hydraulic system; and in response to determining that agiven pressure value for a given deployment event in the pruned datasetsatisfies a low pressure threshold and that a change in pressure valuesover time during the given deployment event satisfies a pressure slopethreshold, generating a service message for the hydraulic system.

In one aspect, in combination with any example method above or below,pruning the dataset comprises selecting the subset of the dataset basedon at least one of: wherein the plurality of deployment events selectedlast for at least a predetermined amount of time; wherein the pluralityof deployment events selected include a deployment condition; andwherein the pressure data during the plurality of deployment eventsselected satisfy a valid-pressure threshold.

In one aspect, in combination with any example method above or below,pruning the dataset further comprises selecting the plurality ofdeployment events based on a difference between a maximum pressure valuein a particular deployment event and a minimum value in the particulardeployment event satisfying a range threshold.

In one aspect, in combination with any example method above or below,the deployment condition includes at least one of: a given altitude atwhich the deployment events occur; and a given temperature at which thedeployment events occurs.

In one aspect, in combination with any example method above or below,the change in pressure values over time during the given deploymentevent is correlated to a low pressure spike occurring at a start of thegiven deployment event.

In one aspect, in combination with any example method above or below,determining that the given deployment event satisfies the pressure slopethreshold further comprises: determining that a first change in pressurevalues over time during a first deployment event satisfies a firstpressure slope threshold; and determining that a second change inpressure values over time during a second deployment event satisfies asecond pressure slope threshold; wherein the second deployment occurssubsequent to the first deployment; and wherein the second pressureslope threshold is greater than the first pressure slope threshold.

In one aspect, in combination with any example method above or below,the hydraulic system operates landing gear of an aircraft.

In another embodiment, the present disclosure provides a non-transitorycomputer-readable storage device including processor executableinstructions that enable a processor to perform operations for faultprediction in hydraulic systems comprising: pruning a dataset byselecting a subset of the dataset for use as a pruned dataset, whereinthe pruned dataset includes pressure data for a hydraulic systemcorrelated in time with command signals for a plurality of deploymentevents of the hydraulic system; and in response to determining that agiven pressure value for a given deployment event in the pruned datasetsatisfies a low pressure threshold and that a change in pressure valuesover time during the given deployment event satisfies a pressure slopethreshold, generating a service message for the hydraulic system.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, pruning the datasetcomprises selecting the subset of the dataset based on at least one of:wherein the plurality of deployment events selected last for at least apredetermined amount of time; wherein the plurality of deployment eventsselected include a deployment condition; and wherein the pressure dataduring the plurality of deployment events selected satisfy avalid-pressure threshold.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, pruning the datasetfurther comprises selecting the plurality of deployment events based ona difference between a maximum pressure value in a particular deploymentevent and a minimum value in the particular deployment event satisfyinga range threshold.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, the deploymentcondition includes at least one of: a given altitude at which thedeployment events occur; and a given temperature at which the deploymentevents occurs.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, the change in pressurevalues over time during the given deployment event is correlated to alow pressure spike occurring at a start of the given deployment event.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, determining that thegiven deployment event satisfies the pressure slope threshold furthercomprises: determining that a first change in pressure values over timeduring a first deployment event satisfies a first pressure slopethreshold; and determining that a second change in pressure values overtime during a second deployment event satisfies a second pressure slopethreshold; wherein the second deployment occurs subsequent to the firstdeployment; and wherein the second pressure slope threshold is greaterthan the first pressure slope threshold.

In one aspect, in combination with any example non-transitorycomputer-readable storage device above or below, the hydraulic systemoperates landing gear of an aircraft.

In a further embodiment, the present disclosure provides a system forfault prediction in hydraulic systems, comprising: a processor; and amemory, the memory including instructions that, when executed by theprocessor, enable the processor to: prune a dataset by selecting asubset of the dataset for use as a pruned dataset, wherein the pruneddataset includes pressure data for a hydraulic system correlated in timewith command signals for a plurality of deployment events of thehydraulic system; and in response to determining that a given pressurevalue for a given deployment event in the pruned dataset satisfies a lowpressure threshold and that a change in pressure values over time duringthe given deployment event satisfies a pressure slope threshold,generate a service message for the hydraulic system.

In one aspect, in combination with any example system above or below,pruning the dataset comprises selecting the subset of the dataset basedon at least one of: wherein the plurality of deployment events selectedlast for at least a predetermined amount of time; wherein the pluralityof deployment events selected include a deployment condition; andwherein the pressure data during the plurality of deployment eventsselected satisfy a valid-pressure threshold.

In one aspect, in combination with any example system above or below,pruning the dataset further comprises selecting the plurality ofdeployment events based on a difference between a maximum pressure valuein a particular deployment event and a minimum value in the particulardeployment event satisfying a range threshold.

In one aspect, in combination with any example system above or below,the deployment condition includes at least one of: a given altitude atwhich the deployment events occur; and a given temperature at which thedeployment events occurs.

In one aspect, in combination with any example system above or below,the change in pressure values over time during the given deploymentevent is correlated to a low pressure spike occurring at a start of thegiven deployment event.

In one aspect, in combination with any example system above or below,determining that the given deployment event satisfies the pressure slopethreshold further comprises: determining that a first change in pressurevalues over time during a first deployment event satisfies a firstpressure slope threshold; and determining that a second change inpressure values over time during a second deployment event satisfies asecond pressure slope threshold; wherein the second deployment occurssubsequent to the first deployment; and wherein the second pressureslope threshold is greater than the first pressure slope threshold.

In one aspect, in combination with any example system above or below,the hydraulic system operates landing gear of an aircraft.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toaspects, some of which are illustrated in the appended drawings.

FIGS. 1A and 1B illustrate a hydraulic cylinder in cross-sectional viewsof a first state and in a second state, according to embodimentsdescribed herein.

FIGS. 2A-C are time series of a various deployment events, according toembodiments described herein.

FIG. 3 is a block diagram of a prefault detection system, according toone embodiment described herein.

FIG. 4 is a flowchart illustrating a method for detecting whether ahydraulic system is in a prefault state, according to one embodimentdescribed herein.

FIG. 5 is a flowchart illustrating a method for pruning a dataset foruse in detecting whether a hydraulic system is in a prefault state,according to one embodiment described herein.

DETAILED DESCRIPTION

The present disclosure relates fault prediction, and more specificallyto improvements in efficiency and reliability for computing devices usedto determine when hydraulic systems are in a prefault state. Ashydraulic systems are often used in subsystems of various complexsystems, such as the breaking subsystems of vehicles, landing gearsubsystems of aircraft, extension subsystems of lifts, etc., the properfunctioning of the hydraulic system may be essential for the non-faultoperation of the complex system. By knowing when the hydraulic system isapproaching a fault state (i.e., when the hydraulic system has entered aprefault state), maintenance may be scheduled to avoid unscheduleddowntime or unexpected faults in the complex system.

FIGS. 1A and 1B illustrate an example hydraulic cylinder 100 incross-sectional views of a first state 101 and in a second state 102.The example hydraulic cylinder 100 is presented in a simplified form tointroduce and illustrate various concepts discussed in greater detailelsewhere in the present disclosure. The present disclosure may beapplied to hydraulic systems with different hydraulic elements than thehydraulic cylinder 100 of FIGS. 1A and 1B, which is provided herein as anon-limiting example.

The example hydraulic cylinder 100 includes a shell 110 and a piston120. The shell 110 defines chamber 130 in which a hydraulic fluid andthe piston 120 are present. The piston 120 divides the chamber 130 intoa first portion 131 and a second portion 132, which may change inrelative volume of the chamber 130 as the piston 120 moves. The piston120 extends into the chamber 130 via a rod port 111 in the shell 110,which has a (typically circular) cross-section of sufficient area toaccommodate the piston 120, and may include various seals or bushings toretain the hydraulic fluid in the chamber 130 as the piston 120 movesinto and out of the chamber 130.

A first fluid port 141 and a second fluid port 142 are also defined inthe shell 110 that allow an external force imparted on the hydraulicfluid to transfer to the piston 120 to affect a position of the piston120 in the chamber 130. For example, FIG. 1A illustrates hydraulic fluidflowing into (and growing) the first portion 131 via the first fluidport 141 and out of the second portion 132 via the second fluid port142, which causes the piston 120 to extend from the chamber 130. Incontrast, FIG. 1B illustrates hydraulic fluid flowing into the secondportion 132 via the second fluid port 142 and out of the first portion131 via the first fluid port 141, which causes the piston 120 to retractinto the chamber 130.

A pump (not illustrated) may be connected to the hydraulic cylinder 100via one or more of the first fluid port 141 and the second fluid port142 as an external pressure source to affect the flow of the hydraulicfluid and thereby control a distance than the piston 120 extendsrelative to the shell 110. The piston 120 extends out of or retractsinto the chamber 130 based on the force transferred via the hydraulicfluid, which may be represented by the formula: F=P·A, where F is theforce exerted on the piston 120, P is pressure exerted on the hydraulicfluid, and A is the area over which the pressure is applied (e.g., thefacial area of the piston 120). One or more pressure sensors 150 may bedeployed in the chamber 130 at various points to measure the pressureexperience within the chamber 130, and may be wired or wirelesslyconnected to a computing device (not illustrated) to report pressurereadings to.

An individual extension or retraction of a piston 120 relative to theshell 110 may be referred to herein as a deployment event. During adeployment event, due to inertia, the properties of the externalpressure source, lack of hydraulic fluid, ambient temperature/pressureaffecting the volume or compressibility of the hydraulic fluid, acontaminant in the hydraulic fluid (with a higher coefficient oncompressibility than the hydraulic fluid), the hydraulic fluid having acoefficient of compressibility greater than zero, etc., the pressureapplied to the hydraulic fluid and/or the force applied to the piston120 may vary. For example, the hydraulic fluid may start at a restingpressure P1 and climb to an active pressure P2 during the course of adeployment event. In another example, the hydraulic fluid begins adeployment event at pressure P1, dips to pressure P2 during thedeployment event, and returns to pressure P1 at the end of thedeployment event. The pressure sensor 150 measures these pressures overthe course of the deployment event so that a prefault state in thehydraulic system may be identified.

FIGS. 2A-C are time series of a various deployment events. In eachdeployment event, the control signal 210 for the deployment event isshown in correlation with a pressure reading 220 taken by pressuresensors 150 during the deployment event. FIG. 2A illustrates a timeseries for a deployment event for a hydraulic system in a nominal state,FIG. 2B illustrates a time series for a deployment event for a hydraulicsystem in a pre-fault state, and FIG. 2C illustrates a time series for adeployment event for a hydraulic system in a faulted state.

In FIG. 2A, the control signal 210 is correlated with a pressure reading220 from time t₀ to time t₉. The control signal 210 is in a static state(e.g., logical zero) from time t₀ to time t₃, transitions to a deployingstate (e.g., logical one) from t₃ to t₄, is in the deploying state fromt₄ to t₆, transitions to the static state from t₆ to t₇, and remains inthe static state from t₇ to t₉. A hydraulic system controlled by thecontrol signal 210 may be in a one state (either extended or retracted)when the control signal 210 is in the static state, but transitions fromthat state to a different state (e.g., from extended to retracted orfrom retracted to extended) when the control signal 210 is in thedeploying state. One or more pumps connected with the chamber 130 maypressurize the hydraulic fluid therein to move a piston 120 to anextended/retracted state starting at t₃ and concluding at t₇, and apressure sensor 150 measures the pressure of the hydraulic fluid attimes t₀ to t₉ to provide the pressure reading 220 correlated with thecontrol signal 210. Although some variation in the pressure is expected,the pressure the hydraulic system over the course of time for thedeployment event from t₀ to t₉ remains relatively constant (e.g., within±10% of a nominal pressure value). In the illustrated example, thepressure at time t₀ is approximately 2500 psi (pounds per square inch),and it approximately 2500 psi at time t₉, and dips at time t₃, but dipsless than 10% from 2500 psi (e.g., less than 250 psi).

In FIG. 2B, the control signal 210 is correlated with a pressure reading220 from time t₀ to time t₉. The control signal 210 is in a static state(e.g., logical zero) from time t₀ to time t₃, transitions to a deployingstate (e.g., logical one) from t₃ to t₄, is in the deploying state fromt₄ to t₆, transitions to the static state from t₆ to t₇, and remains inthe static state from t₇ to t₉. A hydraulic system controlled by thecontrol signal 210 may be in a one state (either extended or retracted)when the control signal 210 is in the static state, but transitions fromthat state to a different state (e.g., from extended to retracted orfrom retracted to extended) when the control signal 210 is in thedeploying state. One or more pumps connected with the chamber 130 maypressurize the hydraulic fluid therein to move a piston 120 to anextended/retracted state starting at t₃ and concluding at t₇, and apressure sensor 150 measures the pressure of the hydraulic fluid attimes t₀ to t₉ to provide the pressure reading 220 correlated with thecontrol signal 210. Unlike the nominal state for the pressure reading220 in FIG. 2A, the pressure reading 220 in FIG. 2B includes a spike inthe pressure measured in the hydraulic system. The spike in pressure isobserved occurring from time t₄ to t₅; lagging transition in the controlsignal 210 from t₃ to t₄, although the precise timing of the spikerelative to the transition in the control signal 210 may vary indifferent embodiments.

The spike in pressure in the pressure reading 220 is analyzed todetermine whether the hydraulic system is in a prefault state. Thelowest point of the pressure spike is determined, and may be referred toas the spiked pressure value 221. The spiked pressure value 221 may becompared against a pressure threshold to determine whether the pressureof the hydraulic fluid satisfies a low-pressure threshold forconsideration for whether the deployment event indicates the hydraulicsystem is in a prefault state. The spiked pressure value 221 may also becompared against a fault-pressure threshold 230 (shown in FIG. 2C),lower than the low-pressure threshold, to determine when the hydraulicsystem is in a faulted state rather than a prefault state. For example,for a hydraulic system with a nominal pressure of X psi, a low-pressurethreshold may be set to ⅞·X psi and a fault-pressure threshold 230 maybe set to ¾·X psi (e.g., a nominal pressure of 2400 psi, a low-pressurethreshold of 2100 psi, a fault-pressure threshold 230 of 1800 psi).

The spiked pressure value 221 (and associated low-pressure threshold)may not be reliable alone in determining whether a hydraulic system isin a prefault state. For example, vibration in the hydraulic system,calibration drift, external temperature and pressure effects, pump lag,inertia of the piston 120, and other effects of operating a hydraulicsystem may all affect the accuracy of values collected by a pressuresensor 150. To reduce the amount of false determinations of whether aprefault state exists in a hydraulic system from using a low-pressurethreshold alone as a detection mechanism, the slope 222 of the pressurespike is used in conjunction with the spiked pressure value 221.

The slope 222 from an initial pressure value to the spiked pressurevalue 221 is determined for the pressure spike to aid in determiningwhen the hydraulic system is in a prefault state. The initial pressurevalue for determining the slope 222 may be selected based on the nominalpressure for the hydraulic system, the pressure measured at time t₃(i.e., when the control signal 210 indicates the beginning of adeployment event), the highest pressure value measured between times t₀to t₃, the average pressure value from times t₀ to t₃, etc. The changefrom the selected initial pressure value to the spiked pressure value221 over time (e.g., from time t₄ to time t₅ in the present example)defines the slope 222, which may be compared against a pressure slopethreshold to determine when the slope 222 indicates that the hydraulicsystem is in a prefault state. For example, if the pressure values inthe illustrated pressure reading 220 of FIG. 2B are 2500 psi at time t₄and 1500 psi at time t₅ (the spiked pressure value), and the timebetween t₄ and t₅ represents 100 ms, the slope 222 is measured to be−10,000 psi/s.

By comparing the spiked pressure value 221 and the slope 222 againstrespective thresholds to determine whether the hydraulic system is in aprefault state, a more reliable determination may be made—and madesooner—than if only one of the spiked pressure value 221 or the slope222 were used. In a case, a hydraulic system may be determined to be ina nominal state despite a slope 222 having a large enough value tosatisfy a pressure slope threshold due to the spiked pressure value 221not satisfying the low-pressure threshold, because a short time betweenthe start of the deployment event and the spiked pressure value 221occurring may in a large value of the slope 222 (e.g., for a constantΔpressure, a smaller Δtime will result in a sharper slope value). In asecond example, a hydraulic system may be determined to be in a nominalstate despite a spiked pressure value 221 having a low enough value tosatisfy a low-pressure threshold due to the slope 222 not satisfying thepressure slope threshold. For example, the low-pressure value may be sethigh, the pumps or pressure generating devices may have unevencapabilities to extend/retract the hydraulic system (which may result inpressure waves), the pressure sensor 150 may be affected by vibration,temperature, or external pressure in its accuracy, the calibration forthe pressure sensor 150 may cause drift in the measured pressure values,the speed/acceleration of the complex system in which the hydraulicsystem is part of may affect a force on the pressure sensor 150, etc.that may make relying solely on the spiked pressure value 221 andlow-pressure threshold less accurate in identifying prefault states thanusing both the spiked pressure value 221 and the slope 222.

Additionally, changes in the value of the slope 222 over time betweendifferent deployment events may be used to determine when the hydraulicsystem is approaching a fault state. For example, a consistent increasein the value of the slope 222 in n or more deployment events may becompared against a trend threshold to determine when one or morecomponents in the hydraulic system are beginning to display signsleading up to a fault state.

As will be appreciated, the numbers and timing used in the aboveexamples are provided as non-limiting examples. The various pressures,time scales, and thresholds may be varied in different embodiments toaccount for different use cases, measurement margins, and userpreferences. The values assigned for various thresholds may bedetermined by user input (e.g., from a subject matter expert) or may bedeveloped from historic operational data in a machine learning model.For example, pressure data from the n deployment events prior to a faultstate occurring may be analyzed to develop or set values for thethresholds to detect when the hydraulic system is in a prefault state.

In FIG. 2C, the control signal 210 is correlated with a pressure reading220 from time t₀ to time t₉. The control signal 210 is in a static state(e.g., logical zero) from time t₀ to time t₃, transitions to a deployingstate (e.g., logical one) from t₃ to t₄, is in the deploying state fromt₄ to t₆, transitions to the static state from t₆ to t₇, and remains inthe static state from t₇ to t₉. A hydraulic system controlled by thecontrol signal 210 may be in a one state (either extended or retracted)when the control signal 210 is in the static state, but transitions fromthat state to a different state (e.g., from extended to retracted orfrom retracted to extended) when the control signal 210 is in thedeploying state. One or more pumps connected with the chamber 130 maypressurize the hydraulic fluid therein to move a piston 120 to anextended/retracted state starting at t₃ and concluding at t₇, and apressure sensor 150 measures the pressure of the hydraulic fluid attimes t₀ to t₉ to provide the pressure reading 220 correlated with thecontrol signal 210.

In various embodiments, a hydraulic system may be determined to be inthe faulted state in response to one or more of the pressure reading 220falling below a fault-pressure threshold 230 and/or the pressure reading220 not returning to a nominal value within a predetermined amount oftime. FIG. 2C illustrates a spike in the pressure that drops below afault-pressure threshold 230 (illustrated at 1000 psi) and does notrecover (if at all) until after t₉. In various embodiments, a pressurereading 220 that does not return to a nominal reading within n secondsof the deployment event beginning (e.g., by time t₃+n) or of thedeployment event ending (e.g., by time t₇+n) may be determined toindicate a faulted hydraulic system, regardless of the slope or thespiked pressure value exhibited by the pressure reading 220.

FIG. 3 is a block diagram of an example prefault detection system 300.The prefault detection system 300 is a computing device, which includesa processor 310 and a memory 320. The processor 310 retrieves andexecutes programming instructions stored in the memory 320 as well asstores and retrieves application data residing in the memory 320. A busis used to transmit programming instructions and application databetween processor 310, memory 320, I/O devices, and a network interface(not shown) to communicate with external devices. External devices arecomputing devices that include, but are not limited to: on-boardcomputers of complex systems, external operator devices (e.g., devicesassociated with maintenance personnel), diagnostic systems, etc.

The processor 310 generally represents any piece of computer hardwarethat is capable of processing information such as, for example, data,computer programs and/or other suitable electronic information. Theprocessor 310 is composed of a collection of electronic circuits some ofwhich may be packaged as an integrated circuit or multipleinterconnected integrated circuits (an integrated circuit at times morecommonly referred to as a “chip”). The processor 310 is configured toexecute computer programs, which may be stored onboard the processor orotherwise stored in the memory 320 (of the same or another apparatus).The processor 310 may represent a number of processors, amulti-processor core or some other type of processor, depending on theparticular implementation. Further, the processor 310 may be implementedusing a number of heterogeneous processor systems in which a mainprocessor is present with one or more secondary processors on a singlechip. As another illustrative example, the processor 310 may be asymmetric multi-processor system containing multiple processors of thesame type. In yet another example, the processor 310 may be embodied asor otherwise include one or more application-specific integratedcircuits (ASICs), field-programmable gate arrays (FPGAs) or the like.Thus, although the processor 310 may be capable of executing a computerprogram to perform one or more functions, the processor of variousexamples may be capable of performing one or more functions without theaid of a computer program.

The memory 320 generally represents any piece of computer hardware thatis capable of storing information such as, for example, data, computerprograms (e.g., computer-readable program code) and/or other suitableinformation either on a temporary basis and/or a permanent basis. Thememory 320 may include volatile and/or non-volatile memory, and may befixed or removable. Examples of suitable memory include random accessmemory (RAM), read-only memory (ROM), a hard drive, a flash memory, athumb drive, a removable computer diskette, an optical disk, a magnetictape or some combination of the above. Optical disks may include compactdisk—read only memory (CD-ROM), compact disk-read/write (CD-R/W),digital versatile disk (DVD), or the like. Although shown as a singleunit, memory 320 may be a combination of fixed and/or removable storagedevices, such as fixed disc drives, removable memory cards, or opticalstorage, network attached storage (NAS), or a storage area-network(SAN). Additionally, although shown as a component of the prefaultdetection system 300, the memory 320 may also include computer hardwaredisposed remotely from the prefault detection system 300, such as, forexample, external hard drives, networked storage, distributed systemsand databases, cloud storage, and the like.

In various instances, the memory 320 may be referred to as acomputer-readable storage medium. The computer-readable storage mediumis a non-transitory device capable of storing information, and isdistinguishable from computer-readable transmission media, such aselectronic transitory signals capable of carrying information from onelocation to another. Computer-readable media as described herein maygenerally refer to a computer-readable storage media orcomputer-readable transmission media.

In addition to the memory 320, the processor 310 may also be connectedto one or more interfaces for displaying, transmitting and/or receivinginformation. The interfaces may include a communications interface(e.g., communications unit) and/or one or more user interfaces oneexample of which may be a network interface. The network interface maybe configured to transmit and/or receive information, such as to and/orfrom another apparatus(es), network(s) or the like. The networkinterface may be configured to transmit and/or receive information byphysical (wired) and/or wireless communications links. Examples ofsuitable communication interfaces include a network interface controller(NIC), wireless NIC (WNIC) or the like.

The user interfaces may include a display and/or one or more user inputinterfaces (e.g., input/output unit). The display may be configured topresent or otherwise display information to a user, suitable examples ofwhich include a liquid crystal display (LCD), light-emitting diodedisplay (LED), plasma display panel (PDP) or the like. The user inputinterfaces may be wired or wireless, and may be configured to receiveinformation from a user into the apparatus, such as for processing,storage and/or display. Suitable examples of user input interfacesinclude a microphone, image or video capture device, keyboard or keypad,joystick, touch-sensitive surface (separate from or integrated into atouchscreen), biometric sensor or the like. The user interfaces mayfurther include one or more interfaces for communicating withperipherals such as printers, scanners or the like.

The memory 320 contains a database of an operational dataset 330, apruned data extractor 340, a pressure analyzer 350, a service messagegenerator 360, and an operating system 370. Generally, the operatingsystem 370 represents software that is configured to manage computinghardware and software resources on the prefault detection system 300.The operating system 370 may further provide computing services forsoftware applications executing on the prefault detection system 300.

The operational dataset 330 provides a repository of operational datafor the complex system. For aircraft, the operational data may includeoperational data provided from a Flight Data Recorder (FDR), QuickAccess Record (QAR), Continuous Parameter Logging System (CPL), and theEnhanced Airborne Flight Recorder (EAFR). The operational data may begathered from several complex systems (e.g., more than one aircraft)over several periods of time, and are identifiable by the parametersmeasured, when the parameters were measured, a phase of operation duringwhich the parameters were measured, the individual complex system fromwhich the parameters were measured, and the like. Generally, theoperational data are a collection of time-series measurements ofpressures and commands related to hydraulic systems. Additionally, anymaintenance messages or alert conditions associated with a givenparameter may be stored in association with the operational data in theoperational dataset 330.

Each hydraulic system may include one or more pressure sensors 150 whosedata are correlated in time with one or more command systems foroperation the hydraulic systems. For example, a hydraulic system mayinclude two pressure sensors whose pressure data are correlated with acontroller system for the complex system that determines when to deploythe first hydraulic system. The data from the hydraulic systems andcontrol systems may be further correlated with data collected from othersubsystems of the complex system, such as, for example, altimeter data,temperature data, speed data, humidity data, etc. Data from each of thesystems may be sampled from the sensors according to a shared systemclock or timestamped to correlate the disparate data with one another ina time series.

According to one embodiment, the pruned data extractor 340 analyzes thepressure and command data held in the operational dataset 330 to selectand extract a subset of pruned data from the operational dataset 330that includes pressure data and command timing data for severaldeployment events that meet various criteria. For example, a complexsystem may have data related to several deployment events, but some ofthose deployment events may be correlated to additional data that makethe data unreliable or undesirable for analysis. For example, pressuredata gathered for a partial deployment event (e.g., a deployment eventlasting less than a predetermined amount of time), gathered at when adeployment condition is not satisfied (e.g., based on altitude, externaltemperature, external humidity), or when the hydraulic system hasexperienced a non-conformance (e.g., a fault in a pressure sensor 150, adata loss/corruption), data from those deployment events may be excludedfrom the pruned data. In some embodiments, the operational data isfurther pruned to reduce the amount of data to a deployment window. Forexample, with a hydraulic subsystem, a control signal may extend orretract a given hydraulic, and a deployment window may define thatoperational data related to the hydraulic subsystem are collected for nseconds before and after deployment is signaled. Data from outside ofthis window may remain unselected so that data in the deployment windoware included in the pruned data.

The pressure analyzer 350 may detect faults in the hydraulic subsystemsof the complex systems from the pruned data received from the pruneddata extractor 340 and develop a model that identifies when a hydraulicsystem is in a prefault state. The model may be constructed in asupervised state based on the historical operational data and indicatedfault states. The pruned data extractor 340 may provide the pruned datato the pressure analyzer 350, which determines whether the pruned dataindicate that the hydraulic subsystem is exhibiting values for aprefault state. The model may associate prefault states in the datagathered in the n deployment events before an indicated fault states(e.g., the operational data gathered in last five flights of an aircraftbefore a fault are associated with a prefault state). Stateddifferently, as the values indicated by the currently analyzed pruneddata approach the values of operational data previously linked to alead-up to a fault state in the hydraulic subsystem, the pressureanalyzer 350 may output a notification that the hydraulic subsystem isin a prefault state. In a second example, a prefault state is associatedwith patterns in the operational data gathered in an individualdeployment event. In this way the pressure analyzer 350 may detect whena hydraulic system is in a prefault state based on trending data valuesthat have historically been associated with a lead-up to fault states aswell as individual deployment events that presage a fault state even ifa subsequent deployment event does not presage an upcoming fault state.

In some embodiments, the prefault detection system 300 generates anotification in response to an analysis provided by the pressureanalyzer 350 that indicates whether the hydraulic subsystem is in aprefault or a nominal state. A service message generator 360 maygenerate a notification that is transmitted to an external device as analert, stored for later transmittal, or logged in the operationaldataset 330 in association with the operational data for the complexsystem and/or deployment event for which a prefault state is detected.In various embodiments, service message generator 360 transmits theservice message to a maintenance scheduler so that preventativemaintenance may be scheduled (or co-scheduled with other maintenance),so that unscheduled downtime of the complex system may be reducedrelative to strategies that rely on service life and/or detectingfaulted systems.

FIG. 4 is an example flowchart showing high level operations for amethod 400 for detecting whether a hydraulic system is in a prefaultstate. Method 400 begins with block 410, where a dataset is pruned. Anoperational dataset 330 may include several groups of data that havevarious levels of relevance in determining whether a given hydraulicsystem within a complex system is in a prefault state. A pruned dataextractor 340, therefore, may select a subset of the operational datastored in the operation dataset 330 to provide as a pruned dataset. Thepruned dataset is reduced in size (i.e., an amount of data) from theoperational dataset 330 by removing various data from consideration. Thecriteria used to select the pruned dataset from the operational dataset330 are discussed in greater detail in regard to FIG. 5.

At block 420, a determination is made regarding whether a givendeployment event includes a spiked pressure value 221 that satisfies alow-pressure threshold. If the determination for a given deploymentevent is positive, method 400 proceeds to block 430. Otherwise, method400 may conclude with a determination that the hydraulic system is notin a prefault state for the given deployment event.

At block 430, a determination is made regarding whether the slope 222satisfies a prefault state threshold. The pressure analyzer 350 maycompare the value of the slope 222 against a pressure slope threshold todetermine whether an individual deployment event includes a sharp enoughslope to indicate a prefault state in the hydraulic system. The pressureanalyzer 350 may also compare the value of the slope 222 with priorvalues of the slope 222 for prior deployment events to detect trends inthe value of the slope 222 that show increasing sharpness of the slope222, such that if a given number of deployment events show increasingvalues in slope 222 satisfy a trend threshold, a prefault state isidentified. If the determination based on the slope 222 is positive,method 400 proceeds to block 440. Otherwise, method 400 may concludewith a determination that the hydraulic system is not in a prefaultstate for the given deployment event.

At block 440, a service message generator 360 generates a servicemessage for the hydraulic system determined to be in a prefault stateaccording to the spiked pressure value 221 satisfying a low-pressurethreshold (per block 420) and the slope 222 satisfying a prefault statethreshold (per block 430). In some embodiments, the service message istransmitted as an alert detailing which the pressure values and slopesthat indicate that the hydraulic system is determined to be in aprefault state. In some embodiments, the prefault detection system 300transmits the notification of a prefault state to a maintenancescheduler system so that a maintenance period may be scheduled for thehydraulic system at a planned time before the hydraulic system enters afault state. Such messages to maintenance schedulers may includehistorical data indicating an average time from detection of theprefault state to a fault in the hydraulic system to help themaintenance scheduler determine when to schedule maintenance and whetheradditional maintenance activities may be co-scheduled. In a furtherembodiment, the service includes a visualization of deployment eventsand which deployments events in a series of deployment events for acomplex system indicate that the hydraulic system is in a prefaultstate.

The alert or maintenance scheduler system may contact a command center,maintenance center or maintenance technician assigned for the complexsystem to address the pre-fault state in the hydraulic system at aspecific time. The specific time may be co-scheduled with othermaintenance events, downtime of the complex system, or a next availablewindow when the complex system is located at the same facility as themaintenance technician. A maintenance technician may inspect and servicethe hydraulic system using the data in the service message to identifywhich hydraulic system was determined to be in the prefault state, andwhat data led to the hydraulic system being classified as in a prefaultstate. After inspecting and/or servicing the hydraulic system, themaintenance technician may then signal the maintenance scheduler orprefault detection system 300 that the hydraulic system has beenreturned to a nominal state or that further maintenance should bescheduled at a later time and the hydraulic system is to be classifiedas in a prefault or faulted state until such further maintenance iscomplete.

Method 400 may then conclude.

FIG. 5 is an example flowchart showing high level operations for amethod 500 for pruning a dataset for use in detecting whether ahydraulic system is in a prefault state. Each of the blocks presented inmethod 500 may be performed by the prefault detection system 300 via apruned data extractor 340 and/or a pressure analyzer 350, and a user mayselect various values for use in the blocks or may select for one ormore of the blocks to be omitted.

At block 510, the prefault detection system 300 prunes the operationaldataset 330 by applying a sampling rate and period for the data ofinterest. A user may select a time period of interest or a granularityin sampling of interest in determining which deployment events toinclude in the pruned dataset and/or how much of the available data toinclude in each analyzed deployment event. For example, the operationaldataset 330 may include several trillion data points gathered acrosstime, of which several billion are data points that were collectedsurrounding deployment events. A user may therefor specify that only thedata points related to a given hydraulic system, data points collectedin the last d days, data points collected in the last n deploymentevents, data points gathered within an s second window of a deploymentevent, or another period are included in the pruned dataset. A user mayalso specify that of the data points included in each deployment event,only a certain portion of those data points are to be used based on asampling rate of the available data points. For example, the data pointsfor a deployment event may be available in the operational dataset 330for every 10 ms, but the user may specify that data collected every 100ms is sufficient for analysis, and the prefault detection system 300 maysample the available data points at a 1:10 rate when creating the pruneddataset. In various additional embodiments, the prefault detectionsystem 300 may also apply various other sampling rates and dataretention periods to build and curate the operational dataset 330 bydetermining when to receive data from the various subsystems of thecomplex system, when to sample incoming analog data, and how long toretain collected data.

At block 520, the prefault detection system 300 prunes the operationaldataset 330 by excluding deployment events with a duration less than aduration threshold. A deployment event that lasts less than apredetermined amount of time (e.g., less than X seconds) may be adeployment event that was cancelled before completion. For example, anoperator may have inadvertently pressed a controller to initiate adeployment event, and cancelled or reversed the deployment event beforecompletion. A deployment event that lasts less than the predeterminedamount of time may also be the result of minor adjustment of thehydraulic system. For example, an operator of a hydraulic lift mayinitiate a deployment event to raise a platform of the lift by severalfeet, and then initiate a smaller deployment event to adjust the heightof a platform by a few inches. The short-duration deployment events maynot be representative of deployment events lasting X or more seconds,due to pressure waves, the effects of overcoming inertia in thehydraulic system, the effects of starting/stopping hydraulic pumps, orthe rapid switch in whether the deployment event is extending/retractingthe piston 120, and therefore may be excluded from the pruned dataset.

At block 530, the prefault detection system 300 prunes the operationaldataset 330 by excluding deployment events that were gathered when thecomplex system was experiencing a specified deployment condition. Insome embodiments, a user may specify various deployment conditions thatmay be associated with unreliable data so that affected deploymentevents may be excluded from the pruned dataset. In other embodiments, auser may specify various deployment conditions that may be associatedwith data of particular interests so that data points not associatedwith the deployment condition are excluded from the pruned dataset.Examples of deployment conditions may include a direction of deployment(e.g., exclude/include extension or retraction deployment events), anexternal temperature range, an external barometric pressure, a weathercondition (e.g., wind speed, precipitation), a travel speed of thecomplex system, an altitude of the complex system, whether a back-up orsecondary hydraulic pump was employed during the deployment event, etc.

At block 540, the prefault detection system 300 prunes the operationaldataset 330 by excluding deployment events where the pressure values forthe deployment event fall outside of a predefined range. In variouscases, the pressure values falling outside of the defined range mayindicate a fault in the pressure sensor 150 or a link from the pressuresensor 150 to a sampler for the operational dataset 330. For example, adeployment event with pressure values of zero may indicate that thepressure sensor 150 is inoperable or disconnected, and thus the data areunreliable. In another example, a deployment event with pressure valuesfar above the operating pressure for the hydraulic system may indicatethat the pressure sensor 150 is mis-calibrated or that the data arecorrupted, and thus the data are unreliable. In some cases, the pressurevalues falling outside of the defined range may indicate a fault in thehydraulic system; indicating that prior deployment events may be usefulfor analysis for a prefault state, but that the current deployment eventis not in the prefault state (by the definition of pre-fault). The usermay specify various pressures, both high and low, to define thevalid-pressure thresholds for a pressure range for deployment events toinclude for analysis in the pruned dataset.

At block 550, the prefault detection system 300 prunes the operationaldataset 330 by excluding deployment events with differences (referred toas AP) between the maximum recorded pressure value and the minimumrecorded pressure value for the given deployment event that do notsatisfy a predefined threshold. A pressure value in the hydraulic systemthat does not change more than the predefined threshold for AP may beindicative of a hydraulic system in the nominal state (e.g., with asmall spiked pressure value 221), a hydraulic system in the faultedstate (e.g., a hydraulic pump does not affect the hydraulic fluid), ormay be indicative of unreliable pressure data (e.g., a pressure sensor150 is “stuck” on a given value). In cases in which the AP does notsatisfy the threshold, the data for such deployment events may beexcluded from the pruned dataset.

Method 500 may then conclude.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a DVD, a memory stick, a floppydisk, a mechanically encoded device such as punch-cards or raisedstructures in a groove having instructions recorded thereon, and anysuitable combination of the foregoing. A computer readable storagemedium, as used herein, is not to be construed as being transitorysignals per se, such as radio waves or other freely propagatingelectromagnetic waves, electromagnetic waves propagating through awaveguide or other transmission media (e.g., light pulses passingthrough a fiber-optic cable), or electrical signals transmitted througha wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method, comprising: pruning a dataset byselecting a subset of the dataset for use as a sanitized dataset,wherein the pruned dataset includes pressure data for a hydraulic systemcorrelated in time with command signals for a plurality of deploymentevents of the hydraulic system; and in response to determining that agiven pressure value for a given deployment event in the pruned datasetsatisfies a low pressure threshold and that a change in pressure valuesover time during the given deployment event satisfies a pressure slopethreshold, generating a service message for the hydraulic system.
 2. Themethod of claim 1, wherein pruning the dataset comprises selecting thesubset of the dataset based on at least one of: wherein the plurality ofdeployment events selected last for at least a predetermined amount oftime; wherein the plurality of deployment events selected include adeployment condition; and wherein the pressure data during the pluralityof deployment events selected satisfy a valid-pressure threshold.
 3. Themethod of claim 2, wherein pruning the dataset further comprisesselecting the plurality of deployment events based on a differencebetween a maximum pressure value in a particular deployment event and aminimum value in the particular deployment event satisfying a rangethreshold.
 4. The method of claim 2, wherein the deployment conditionincludes at least one of: a given altitude at which the deploymentevents occur; and a given temperature at which the deployment eventsoccurs.
 5. The method of claim 1, wherein the change in pressure valuesover time during the given deployment event is correlated to a lowpressure spike occurring at a start of the given deployment event. 6.The method of claim 1, wherein determining that the given deploymentevent satisfies the pressure slope threshold further comprises:determining that a first change in pressure values over time during afirst deployment event satisfies a first pressure slope threshold; anddetermining that a second change in pressure values over time during asecond deployment event satisfies a second pressure slope threshold;wherein the second deployment occurs subsequent to the first deployment;and wherein the second pressure slope threshold is greater than thefirst pressure slope threshold.
 7. The method of claim 1, wherein thehydraulic system operates landing gear of an aircraft.
 8. Anon-transitory computer-readable storage device including processorexecutable instructions that enable a processor to perform operationscomprising: pruning a dataset by selecting a subset of the dataset foruse as a pruned dataset, wherein the pruned dataset includes pressuredata for a hydraulic system correlated in time with command signals fora plurality of deployment events of the hydraulic system; and inresponse to determining that a given pressure value for a givendeployment event in the pruned dataset satisfies a low pressurethreshold and that a change in pressure values over time during thegiven deployment event satisfies a pressure slope threshold, generatinga service message for the hydraulic system.
 9. The non-transitorycomputer-readable storage device of claim 8, wherein pruning the datasetcomprises selecting the subset of the dataset based on at least one of:wherein the plurality of deployment events selected last for at least apredetermined amount of time; wherein the plurality of deployment eventsselected include a deployment condition; and wherein the pressure dataduring the plurality of deployment events selected satisfy avalid-pressure threshold.
 10. The non-transitory computer-readablestorage device of claim 9, wherein pruning the dataset further comprisesselecting the plurality of deployment events based on a differencebetween a maximum pressure value in a particular deployment event and aminimum value in the particular deployment event satisfying a rangethreshold.
 11. The non-transitory computer-readable storage device ofclaim 9, wherein the deployment condition includes at least one of: agiven altitude at which the deployment events occur; and a giventemperature at which the deployment events occurs.
 12. Thenon-transitory computer-readable storage device of claim 8, wherein thechange in pressure values over time during the given deployment event iscorrelated to a low pressure spike occurring at a start of the givendeployment event.
 13. The non-transitory computer-readable storagedevice of claim 8, wherein determining that the given deployment eventsatisfies the pressure slope threshold further comprises: determiningthat a first change in pressure values over time during a firstdeployment event satisfies a first pressure slope threshold; anddetermining that a second change in pressure values over time during asecond deployment event satisfies a second pressure slope threshold;wherein the second deployment occurs subsequent to the first deployment;and wherein the second pressure slope threshold is greater than thefirst pressure slope threshold.
 14. The non-transitory computer-readablestorage device of claim 8, wherein the hydraulic system operates landinggear of an aircraft.
 15. A system, comprising: a processor; and amemory, the memory including instructions that, when executed by theprocessor, enable the processor to: prune a dataset by selecting asubset of the dataset for use as a pruned dataset, wherein the pruneddataset includes pressure data for a hydraulic system correlated in timewith command signals for a plurality of deployment events of thehydraulic system; and in response to determining that a given pressurevalue for a given deployment event in the pruned dataset satisfies a lowpressure threshold and that a change in pressure values over time duringthe given deployment event satisfies a pressure slope threshold,generate a service message for the hydraulic system.
 16. The system ofclaim 15, wherein pruning the dataset comprises selecting the subset ofthe dataset based on at least one of: wherein the plurality ofdeployment events selected last for at least a predetermined amount oftime; wherein the plurality of deployment events selected include adeployment condition; and wherein the pressure data during the pluralityof deployment events selected satisfy a valid-pressure threshold. 17.The system of claim 16, wherein pruning the dataset further comprisesselecting the plurality of deployment events based on a differencebetween a maximum pressure value in a particular deployment event and aminimum value in the particular deployment event satisfying a rangethreshold.
 18. The system of claim 16, wherein the deployment conditionincludes at least one of: a given altitude at which the deploymentevents occur; and a given temperature at which the deployment eventsoccurs.
 19. The system of claim 15, wherein the change in pressurevalues over time during the given deployment event is correlated to alow pressure spike occurring at a start of the given deployment event.20. The system of claim 15, wherein determining that the givendeployment event satisfies the pressure slope threshold furthercomprises: determining that a first change in pressure values over timeduring a first deployment event satisfies a first pressure slopethreshold; and determining that a second change in pressure values overtime during a second deployment event satisfies a second pressure slopethreshold; wherein the second deployment occurs subsequent to the firstdeployment; and wherein the second pressure slope threshold is greaterthan the first pressure slope threshold.